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国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习

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  • 1 2-Steps Approach for Both Rotor and Bearing Faults Identification in Rotating Machines Using Measured Vibration Responses
       利用测得的振动响应识别旋转机械转子和轴承故障的两步法
  • 2 3D Multibody Simulation of Realistic Rolling Bearing Defects for Fault Classifier Development
       用于故障分类器开发的逼真滚动轴承缺陷三维多体仿真
  • 3 A Batch-normalized Deep Neural Networks and its application in bearing fault diagnosis
       批归一化深度神经网络及其在轴承故障诊断中的应用
  • 4 A Bearing Fault Diagnosis Method Based on Branch Convolution Neural Network
       基于分支卷积神经网络的轴承故障诊断方法
  • 5 A Bearing Fault Diagnosis Method Based on L1 Regularization Transfer Learning and LSTM Deep Learning
       基于L1正则化迁移学习与LSTM深度学习的轴承故障诊断方法
  • 6 A BEARING FAULT DIAGNOSIS METHOD BASED ON VGG SENET-BIGRU GLOBAL ATTENTION
       基于VGG SENET-BIGRU全局注意力的轴承故障诊断方法
  • 7 A Bearing Fault Diagnosis Method with Unsupervised Deep Adaptive Network
       基于无监督深度自适应网络的轴承故障诊断方法
  • 8 A BROAD TRANSFER LEARNING ALGORITHM FOR CLASSIFICATION OF BEARING FAULTS
       轴承故障分类的广义转移学习算法
  • 9 A Channel Attention based Large Kernel Convolutional Neural Network for Bearing Fault Diagnosis
       基于通道注意力的大核卷积神经网络用于轴承故障诊断
  • 10 A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions
       一种适用于不同工况下滚动轴承故障诊断的类级匹配无监督迁移学习网络
  • 11 A comparative Analysis of 1D Convolutional Neural Networks for Bearing Fault Diagnosis
       一维卷积神经网络在轴承故障诊断中的比较分析
  • 12 A Comparative Study Between Bayesian Filters and Autoencoder Networks for Bearing RUL Prediction
       贝叶斯滤波器与自编码器网络在轴承剩余使用寿命预测中的比较研究
  • 13 A Comparison of Deep Transfer Learning Methods on Bearing Fault Detection
       深度迁移学习方法在轴承故障检测中的比较
  • 14 A cross domain deep learning method for rolling bearing fault diagnosis
       用于滚动轴承故障诊断的跨域深度学习方法
  • 15 A Cross Domain Feature Extraction Method for Bearing Fault diagnosis based on Balanced Distribution Adaptation
       基于均衡分布自适应的轴承故障诊断跨域特征提取方法
  • 16 A Data-Driven Approach Based on Artificial Neural Networks for the Detection and Classification of Bearing Anomalies in Power Generation Plants
       基于人工神经网络的数据驱动方法用于发电厂轴承异常的检测与分类
  • 17 A data-driven exploration of multi pad bidirectional adjustable bearings and deep learning model-based optimization
       基于数据的多垫双向可调轴承探索与深度学习模型优化
  • 18 A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions
       一种基于信息融合的深度卷积神经网络方法，用于不同工况下的轴承故障诊断
  • 19 A Deep Correlation Feature Extraction Network: Intelligent Description of Bearing Fault Knowledge for Zero-Sample Learning
       深度相关特征提取网络：用于零样本学习的轴承故障知识智能描述
  • 20 A Deep Learning Approach for Failure Prognostics of Rolling Element Bearings
       滚动轴承故障预测的深度学习方法
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